Enhancing energy efficiency: Design and simulation of air fractionation unit integrated through <scp>LNG</scp> cold energy and two‐stage organic Rankine cycles
Bibliographic record
Abstract
Abstract The study explores air separation processes, proposing an innovative design incorporating liquid natural gas (LNG)'s two‐stage Rankine cycles to address traditional approaches' complexity and energy intensity. Significant wastage of energy during air compression in standard units is recuperated for liquefied natural gas regasification, with a focus on enhancing cold energy recovery, emphasizing cryogenic LNG advantages. Aspen HYSYS (12.1) is used for process modelling and simulation evaluating a combined two‐stage Rankine cycle integrated into air separation. Specific energy requirements for high‐purity oxygen and nitrogen production are reduced to 0.38 and 0.12 kWh/kg, respectively. The integrated Rankine cycle generates 4456.32 kW, which is sufficient for air separation process. Exergy destruction and component efficiency are explored and parametric optimization, revealing LNG variables' significant impact. Economic analysis indicates a fair 5.25‐year payback period. This approach aligns with sustainability goals, providing a compelling efficiency‐enhancing option for the LNG sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".